Assessing uncertainty in geochemical models for core formation in Earth
Assessing uncertainty in geochemical models for core formation in Earth
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DOI：
10.1016/j.epsl.2013.01.014
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发表时间：
2013-03
影响因子：
5.3
通讯作者：
M. Walter;E. Cottrell
中科院分区：
文献类型：
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作者：
M. Walter;E. Cottrell
揭开地球金属铁核形成的条件，可以提供有关地球早期吸积和分化历史的重要信息。成核金属液体和硅酸盐熔体之间亲铁元素分配的多变量统计建模形成了成核物理模型的基础。虽然它似乎很清楚，在深橄榄岩岩浆海洋的核心隔离一般是一致的许多地幔亲铁元素丰度，有相当大的差距，现存的物理模型中的压力，温度和氧逸度的核心形成的关键参数。此外，有正在进行的辩论是否简单的单阶段平衡或更复杂的多阶段吸积模型所需要的分区数据。在这里，我们考虑分区数据的统计回归的变化如何影响核心形成的物理模型的结果。以现有的实验数据集的四个良好的研究亲铁元素（镍，钴，钨和V）为例，我们发现，回归模型施加一个基本的控制物理模型的结果。此外，实验数据目前太不精确，无法区分各种单阶段和连续的核心形成的情况。物理模型开发方面的进展需要更好地分离影响分配系数的自变量，并在高压和高温下验证活动模型，以减少多变量统计模型的全球不确定性。
Unraveling the conditions at which Earth's metallic iron core formed yields important information about Earth's early accretion and differentiation history. Multi-variable statistical modeling of siderophile element partitioning between core-forming metallic liquids and silicate melts form the basis for physical models of core formation. While it seems clear that core segregation in a deep peridotitic magma ocean is generally consistent with many mantle siderophile element abundances, there is considerable disparity among extant physical models in terms of the key parameters of pressure, temperature and oxygen fugacity at which the core formed. Moreover, there is ongoing debate over whether simple single-stage equilibrium or more complex multi-stage accretion models are required by the partitioning data. Here we consider how variations in the statistical regression of partitioning data affect the outcomes of physical models for core formation. Taking extant experimental data sets for four well-studied siderophile elements (Ni, Co, W and V) as examples, we find that the regression model exerts a fundamental control on physical model outcomes. Further, the experimental data are currently too imprecise to discriminate among various single-stage and continuous core formation scenarios. Progress in the development of physical models requires better isolation of the independent variables that affect partition coefficients and verification of activity models at high pressure and temperature in order to reduce the global uncertainty in multi-variable statistical models.